(Received 30 August 2005; accepted 14 November 2005;
published online
24 February 2006)

Abstract -
This simulation study was designed to study the power and type I
error rate in QTL mapping using cofactor analysis in half-sib designs. A
number of scenarios were simulated with different power to identify QTL by
varying family size, heritability, QTL effect and map density, and three
threshold levels for cofactor were considered. Generally cofactor analysis
did not increase the power of QTL mapping in a half-sib design, but
increased the type I error rate. The exception was with small family size
where the number of correctly identified QTL increased by 13% when
heritability was high and 21% when heritability was low. However, in the
same scenarios the number of false positives increased by 49% and 45%
respectively. With a liberal threshold level of 10% for cofactor combined
with a low heritability, the number of correctly identified QTL increased by
14% but there was a 41% increase in the number of false positives.
Also, the power of QTL mapping did not increase with cofactor analysis in
scenarios with unequal QTL effect, sparse marker density and large QTL
effect (25% of the genetic variance), but the type I error rate tended to
increase. A priori, cofactor analysis was expected to have higher power than
individual chromosome analysis especially in experiments with lower power to
detect QTL. Our study shows that cofactor analysis increased the number of
false positives in all scenarios with low heritability and the increase was
up to 50% in low power experiments and with lower thresholds for
cofactors.